{"publication":"Reports on Progress in Physics","article_type":"review","date_created":"2024-04-14T22:01:01Z","author":[{"orcid":"0000-0001-7205-2975","full_name":"Brückner, David","id":"e1e86031-6537-11eb-953a-f7ab92be508d","first_name":"David","last_name":"Brückner"},{"full_name":"Broedersz, Chase P.","first_name":"Chase P.","last_name":"Broedersz"}],"date_published":"2024-04-04T00:00:00Z","abstract":[{"lang":"eng","text":"Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description."}],"volume":87,"type":"journal_article","oa":1,"article_number":"056601","doi":"10.1088/1361-6633/ad36d2","publication_status":"epub_ahead","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"04","year":"2024","title":"Learning dynamical models of single and collective cell migration: a review","citation":{"chicago":"Brückner, David, and Chase P. Broedersz. “Learning Dynamical Models of Single and Collective Cell Migration: A Review.” <i>Reports on Progress in Physics</i>. IOP Publishing, 2024. <a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">https://doi.org/10.1088/1361-6633/ad36d2</a>.","short":"D. Brückner, C.P. Broedersz, Reports on Progress in Physics 87 (2024).","ama":"Brückner D, Broedersz CP. Learning dynamical models of single and collective cell migration: a review. <i>Reports on Progress in Physics</i>. 2024;87(5). doi:<a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">10.1088/1361-6633/ad36d2</a>","mla":"Brückner, David, and Chase P. Broedersz. “Learning Dynamical Models of Single and Collective Cell Migration: A Review.” <i>Reports on Progress in Physics</i>, vol. 87, no. 5, 056601, IOP Publishing, 2024, doi:<a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">10.1088/1361-6633/ad36d2</a>.","apa":"Brückner, D., &#38; Broedersz, C. P. (2024). Learning dynamical models of single and collective cell migration: a review. <i>Reports on Progress in Physics</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">https://doi.org/10.1088/1361-6633/ad36d2</a>","ista":"Brückner D, Broedersz CP. 2024. Learning dynamical models of single and collective cell migration: a review. Reports on Progress in Physics. 87(5), 056601.","ieee":"D. Brückner and C. P. Broedersz, “Learning dynamical models of single and collective cell migration: a review,” <i>Reports on Progress in Physics</i>, vol. 87, no. 5. IOP Publishing, 2024."},"department":[{"_id":"EdHa"}],"license":"https://creativecommons.org/licenses/by/3.0/","date_updated":"2024-04-17T12:17:57Z","language":[{"iso":"eng"}],"status":"public","ddc":["530"],"acknowledgement":"This work was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation)—Project-ID 201269156—SFB 1032 (Project B12). D B B was supported by an NOMIS Fellowship and an EMBO Fellowship (ALTF 343-2022). We thank Joachim Rädler, Alexandra Fink, Erwin Frey, Pierre Ronceray, Ricard Alert, Edouard Hannezo, Henrik Flyvbjerg, Ulrich Schwarz, Joshua Shaevitz, Greg Stephens, Andrea Cavagna, Grzegorz Gradziuk, Fridtjof Brauns, Nikolas Claussen, Tom Brandstätter, Johannes Flommersfeld, Christoph Schreiber, Nicolas Arlt, Matthew Schmitt, Joris Messelink, Federico Gnesotto, Federica Mura, Bram Hoogland, Manon Wigbers, Isabella Graf, Jessica Lober, and many others for inspiring discussions. We also thank Claudia Flandoli for the artwork in figures 1, 5, 8 and 9.","tmp":{"short":"CC BY (3.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"month":"04","scopus_import":"1","project":[{"grant_number":"343-2022","_id":"34e2a5b5-11ca-11ed-8bc3-b2265616ef0b","name":"A mechano-chemical theory for stem cell fate decisions in organoid development"}],"oa_version":"Published Version","quality_controlled":"1","_id":"15315","publisher":"IOP Publishing","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1088/1361-6633/ad36d2"}],"article_processing_charge":"Yes (in subscription journal)","issue":"5","publication_identifier":{"issn":["0034-4885"],"eissn":["1361-6633"]},"intvolume":"        87"}